NeurIPS 2013

Learning to Prune in Metric and Non-Metric Spaces

NeurIPS 2013 searchivarius/NonMetricSpaceLib

Our focus is on approximate nearest neighbor retrieval in metric and non-metric spaces.

DENSITY ESTIMATION

Multi-Task Bayesian Optimization

NeurIPS 2013 HIPS/Spearmint

We demonstrate the utility of this new acquisition function by utilizing a small dataset in order to explore hyperparameter settings for a large dataset.

GAUSSIAN PROCESSES IMAGE CLASSIFICATION

Translating Embeddings for Modeling Multi-relational Data

NeurIPS 2013 Accenture/AmpliGraph

We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces.

LINK PREDICTION

Distributed Representations of Words and Phrases and their Compositionality

NeurIPS 2013 RaRe-Technologies/gensim-data

Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.

Zero-Shot Learning Through Cross-Modal Transfer

NeurIPS 2013 mganjoo/zslearning

This work introduces a model that can recognize objects in images even if no training data is available for the object class.

ZERO-SHOT LEARNING

Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture

NeurIPS 2013 trevorcampbell/dynamic-means

This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters.

Bayesian entropy estimation for binary spike train data using parametric prior knowledge

NeurIPS 2013 pillowlab/CDMentropy

Shannon's entropy is a basic quantity in information theory, and a fundamental building block for the analysis of neural codes.

Stochastic blockmodel approximation of a graphon: Theory and consistent estimation

NeurIPS 2013 airoldilab/SBA

Given a convergent sequence of graphs, there exists a limit object called the graphon from which random graphs are generated.

Generalized Denoising Auto-Encoders as Generative Models

NeurIPS 2013 cycentum/bert-based-text-generation

Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of the data-generating density, in the case where the corruption noise is Gaussian, the reconstruction error is the squared error, and the data is continuous-valued.

DENOISING

Phase Retrieval using Alternating Minimization

NeurIPS 2013 Jay-Lewis/phase_retrieval

Empirically, we demonstrate that alternating minimization performs similar to recently proposed convex techniques for this problem (which are based on "lifting" to a convex matrix problem) in sample complexity and robustness to noise.